USGIF GotGeoint BlogUSGIF promotes geospatial intelligence tradecraft and a stronger community of interest between government, industry, academia, professional organizations and individuals focused on the development and application of geospatial intelligence to address national security objectives.

September 29, 2015

Early this week Will Marshall, Co-Founder and CEO of Planet Labs and previously a scientist at NASA/USRA, traveled to the United Nations Headquarters in New York to represent Planet Labs at the UN Sustainable Development Summit where 17 Sustainable Development Goals (SDGs) were adopted. In his speech Marshall announced that Planet Labs was making $60 million worth of its satellite imagery for select regions openly available and accessible to the global community. The imagery will be made available through a Planet Labs initiative called "Open Regions," under a creative commons license (CC BY-SA), and will be directly accessible online. According to Planet Labs’ analysis, the imagery is relevant to 15 of the UN's 17 SDGs. Some examples mentioned in Will Marshall's UN speech are:

Combating climate change - Planet Lab's imagery can monitor climate change with up-to-date data on the state of the world’s ice caps and carbon stocks.

Ending hunger and establishing food security - Planet Lab's imagery can measure the health of crops in farmers' fields around the world, and provide vital information to them to increase crop yield.

Planet Labs is unique in the satellite imagery world because it relies on many small, inexpensive satellites rather than a small number of large, expensive satellites. The constellation of Planet Labs CubeSats (Doves) returns imagery of Earth with a resolution between 3 and 5 meters. The revisit rate, or frequency with which Dove CubeSats pass over a given area, is unprecedented among existing satellite systems in orbit. Planet Labs' goal is to capture an image of the entire Earth every day.

April 27, 2015

According to a recent article in Science, 132 CubeSats were launched in 2014. The original 1U CubeSat, designed in 1999, was a 10 cm cube weighing less than a kilogram. They could be launched on Russian rockets for about $30,000. NASA has a program called the CubeSat Launch Initiative (CSLI) which provides opportunities for nanosatellites to fly inexpensively as auxiliary payloads on rockets supporting major missions. Currently they can be launched for about $100,000. The economics of these small satellites is so compelling that there is talk of a constellation of CubeSats replacing Landsat 8, which was launched in 2013 at a cost of $855 million. CubeSats are not restricted to low Earth orbit. It is planned to launch 6U CubeSats into orbits around Mars next year. The more generic terms for these satellites is nanosatellites for satellites weighing less than a kilogram and microsatellites for satellites up to 100 kg.

Planet Labs

The company was formed in 2010 under a different name. In April 2013 Planet Labs launched two demonstration satellites, “Dove 1” and “Dove 2”. Planet Labs has launched Flock 1 comprised of 28 CubeSats for Earth observation at an altitude of 400 km. The satellites are intended to provide frequent snapshots of the planet at a resolution of about 5 m, allowing users to track changes such as traffic jams, deforestation, construction progress in close to real time. The primary applications right now are agriculture - monitoring crop productivity, environmental compliance - monitoring restoration after mining, and commercial mapping - identifying new developments that require mapping.

Skybox/Google

Skybox Imaging, acquired by Google, has launched the SkySat-1 and SkySat-2 satellites (each larger than nanosatellites at about 100 kg) which capture sub-meter imagery and HD-video of any spot on earth, multiple times per day to provide timely high-resolution imagery, HD video, and analytics. First imagery can be seen here.

MarketsandMarkets has released a report entitled Nanosatellite and Microsatellite Market [Geographic Information system, Payload, Space Science, Satellite Communication, Satellite Imagery, Remote Sensing, Scientific Research, Reconnaissance, Satellite Launch] - Worldwide Market Forecast (2014 - 2019) in which it attempts to quantify the nano and microsatellite market. Nano and microsatellites includes satellites that weigh about 100 kg (such as the Skybox satellites) and less. According to MarketsandMarkets commercial as well as private ventures have started investing to capitalize on the opportunities presented through low-cost small satellite missions related to communication, earth-observation, remote-sensing, and many others. Integration of commercial-off-the-shelf electronic circuits is accelerating the demand for small satellites. MarketsandMarkets forecasts that the market for nanosatellites and microsatellites will grow at a rate (CAGR) of 21.8% per year from $700 million in 2014 to $1.9 billion in 2019.

November 06, 2014

Nanosatellites for Earth observation are very small, low cost satellites typically weighing kilograms and with volumes of a few liters. NASA has a program called the CubeSat Launch initiative (CSLI) which provides opportunities for nanosatellites to fly inexpensively as auxiliary payloads on rockets supporting major missions. NASA's CubeSats are cube-shaped satellites 10cm x 10cm x 10cm with a volume of about a liter and weighing about 1.3 kilograms.

Three startup satellite companies have already started putting nanosatellite constellations in space, that promise to provide much more frequent revisits per day than existing satellites can provide and at a much lower cost. In April 2013 Planet Labs launched two demonstration satellites, “Dove 1” and “Dove 2”. Planet Labs has already launched 72 cubesats and plans to launch a total 100 of the Earth observing satellites at an altitude of 400 km. The satellites will provide frequent snapshots of the planet at a resolution of about 5 m, allowing users to track changes such as traffic jams, deforestation, construction progress in close to real time.

Skybox Imaging, acquired by Google, has launched the SkySat-1 and SkySat-2 satellites (each larger than nanosatellites at about 100 kg) which are intended to capture sub-meter imagery and HD-video of any spot on earth, multiple times per day to provide timely high-resolution imagery, HD video, and analytics.

Spire Global

In November 2013 two nanosatellites, NanoSatisfi's ArduSat 1 and ArduSat X, supported by Kickstarter crowdfunding were placed in orbit from the International Space Station . NanoSatisfi has since been renamed. Spire Global Inc, has launched 4 out of more than 50 planned remote sensing cubesats into orbit at altitudes of 500–600 km. Spire's remote sensing satellites range from cubesat 1U to 3U and are built with Commercial Off-The-Shelf (COTS) components wherever possible to lower costs. The spacecraft carry multiple sensors including RF sensors that are not restricted by clouds, haze and night.

Spire's business niche is 80 km from any coastline, the three-quarters of the Earth that is either covered by water or considered remote and is not regularly monitored by today's remote-sensing satellites. Spire plans to be able to collect data from any point on Earth every hour.

Spire’s multi-sensor satellites provide a variety of data types such as Automatic Identification System (AIS) service for tracking ships, and weather payloads that measure temperature, pressure and precipitation. Spire plans for a 2 year refresh cycle on each satellite enabling a continual improvement in on-board technology .

Trade monitoring - Virtually all global trade transits through areas of the planet otherwise ignored by traditional remote sensing. Spire can deliver reliable data on every ship in every ocean every hour.

Marine domain awareness - Criminal activities such as illegal shipments and clandestine cargo exchange are some of the issues.

March 11, 2014

A release of 28 Planet Labs CubeSats known as Flock 1 from the International Space Station (ISS) has been completed. The deployments began Feb. 11. The CubeSats, each about the size of a loaf of bread, were released from pods mounted on the end of the space station's robotic arm. The satellites were carried to the ISS aboard a Cygnus cargo spacecraft on an Antares rocket January 9.

The constellation of Planet Labs CubeSats will return imagery of Earth with a resolution between 3 and 5 meters. The revisit rate, or frequency with which Dove CubeSats pass over a given area, is unprecedented among existing satellite systems in orbit. Imagery will be collected at latitudes within 52 degrees of the equator, which encompass expanses north and south of the equator that cover the majority of the world’s populated areas and agricultural regions. The Flock 1 constellation will travel in a lower orbit than most satellites, at a distance between 240 and 400 miles above Earth. For comparison, weather and commercial communications satellites are often given geostationary orbits, which are circular orbits above the Earth’s equator at a distance of approximately 22,236 miles above Earth. According to Planet Labs continuous whole-Earth images have the potential to serve many purposes simultaneously, from a single set of data. According to this source the imagery from the Flock 1 satellites will be avaialble for free access by commercial and humanitarian users. Flock 1 will allow scientists and the public to monitor natural disasters, deforestation, agricultural yields and other environmental changes.

January 15, 2014

A very recent article is Scientific American provides more information about Planet Labs' plans for "continuous whole Earth imagery". The constellation of 28 Earth-imaging satellites called “Flock 1” which just rode into space Jan 9 is comprised of 28 “Doves” each weighing about five kilograms. As part of the payload on an Antares rocket the satellites are on their way to the International Space Station from which they will be individually launched into Earth orbits.

By the end of the month “Flock 1” will be in position to photograph the complete surface of the planet at a resolution of 3-5 meters per pixel. This will require storing the equivalent of a 10-terapixel image. Planet Labs plans for the satellites to provide near-continuous pictures of Earth’s surface. This will make Planet Labs the first to capture high-resolution whole-Earth images nearly continuously. Existing Earth observation satellites can provide higher resolution and more spectral range but they only photograph specific targets which are selected by customers and their revisit times are on the order of a day. In effect customers rent the use of a satellite to capture detailed images of very specific small areas of the Earth at particular times. Planet Labs' constellation will photograph the entire Earth's surface very frequently. According to Planet Labs continuous whole-Earth images have the potential to serve many purposes simultaneously, from a single set of data.

January 10, 2014

In April 2013 Planet Labs launched two demonstration satellites, “Dove 1” and “Dove 2”. November 21 two more satellites Dove 3 and Dove 4 were placed into orbit by a Dnepr rocket. Dove 4 conforms to the 3U CubeSat specification, with a launch mass of 5.2 kg. Basic physical dimensions are 100 mm × 100 mm × 340 mm, with two 260 mm × 300 mm deployable solar arrays. Power storage is provided by Lithium-Ion cells. The batteries will be recharged by solar cells mounted on the body of the satellite and on the two deployable solar panels.

Yesterday January 9 Orbital Sciences Corp. launched its Cygnus cargo spacecraft aboard its Antares rocket at 1:07 p.m. EST Thursday from NASA’s Wallops Flight Facility in Virginia. Its primary mission is to resupply the International Space Station. Solar array deployment has been reported as complete for the Cygnus spacecraft. It is scheduled to rendezvous with the International Space Station on Sunday, Jan. 12.

In addition to the suppplies for the Space Station the Cygnus carries CubeSats for 23 experiments designed by students for a variety of technology demonstrations.

And it also carries a constellation of CubeSat based satellites that will form Planet Labs' 28 satellite Earth observation constellation. These mini-Earth observing satellites will orbit the Earth at an altitide of 400 km. The satellites can provide much more frequent snapshots (revisits) of the same location on Earth than current Earth observation satellites. This means basically 4D (2D/3D+time) allowing users to track changes—from traffic jams to deforestation—in close to real time. The satellites will send their images to at least three ground stations—two in the U.S. and one in the U.K. The data will be processed and uploaded for use by customers almost immediately.

December 11, 2013

SkySat-1 was launched on November 21, 2013 from Yasny, Russia on a Dnepr rocket. SkySat-1 is the first of a planned constellation of small 24 Earth observation satellites. SkySat-1 has a mass of 100 kg and is capable of producing sub-meter resolution imagery and high-definition video. The satellite will operate in a circular orbit at approximately 450 km above the earth, significantly lower than the proposed 617 km high orbit of Worldview-3.

The first images returned from SkySat-1 have been published. They were taken over Perth, Australia, on December 4, 2013 at 10:25AM and are untuned and uncalibrated. The detail is incredible for a satelite weighting almost 30x less than the 2,800 kg Worldview-2.

November 22, 2013

In April 2013 Planet Labs launched two demonstration satellites, “Dove 1” and “Dove 2”. Yesterday two more satellites Dove 3 and Dove 4 were placed into orbit by a Dnepr rocket.

It appears the Dove-3 and 4 mission is a technology demonstration. They will operate from longer lived sun synchronous orbits. Both satellites are licensed to collect images of the Earth and will undertake an experimental mission in an 800 km by 597 km and a 700 km circular orbit, respectively. Dove 3 conforms to the 3U cubesat specification, with a launch mass of 5.2 kg. Basic physical dimensions are 100 mm × 100 mm × 340 mm, with two 260 mm × 300 mm deployable solar arrays. Power storage is provided by Lithium-Ion cells. The batteries will be recharged by solar cells mounted on the body of the satellite and on the two deployable solar panels.

November 20, 2013

Nanosatellites are very small, low cost satellites typically weighing kilograms and with volumes of a few liters.

NASA has a program called the CubeSat Launch initiative (CSLI) which provides opportunities for nanosatellites to fly inexpensively as auxiliary payloads on rockets supporting major missions. NASA's CubeSats are cube-shaped satellites 10cm x 10cm x 10cm with a volume of about a liter and weighing about 1.3 kilograms. The CSLI program promotes innovative technology partnerships among NASA, U.S. industry, and other sectors for the benefit of Agency programs and projects.

Two nanosatellites, NanoSatisfi's ArduSat 1 and ArduSat X, supported by Kickstarter crowdfunding were placed in orbit from the International Space Station yesterday. using a Japanese-built, spring-loaded launcher.

4D (time + 2D/3D) satellite imagery

Two startup satellite companies have already started putting nanosatellite constellations in space, that promise to provide much more frequent revisits per day than existing satellites can provide and at a much lower cost.

In April 2013 Planet Labs launched two demonstration satellites, “Dove 1” and “Dove 2”. In early 2014, Planet Labs plans to launch 28 mini- Earth observing satellites at an altitide of 400 km. The satellites will provide frequent snapshots of the planet at a resolution aof about 5 m, allowing users to track changes such as traffic jams, deforestation, conctruction progress in close to real time.

Skybox Imaging plans to launch a constellation of 24+ satellites (each larger than nanosatellites at about 100 kg) that will capture sub-meter imagery and HD-video of any spot on earth, multiple times per day. Skybox will capture the planet on a near real-time basis to provide a tool for addressing global challenges in areas including security, humanitarian efforts, and environmental monitoring. In both cases it is expected that the cost of the imagery will be signficantly less than current pricing.

October 28, 2013

Just about every industry from construction to utilities to roofers to insurance is being impacted, and often transformed, by the availability of high resolution Earth observation (EO) imagery from satellites, planes and helicopters, and UAVs. Initially 2D, then 3D and in 2014 4D (2D/3D + temporal) imagery is available from many sources. Imagery from international satellites, low cost satellite constellations and UAVs is poised to dramatically reduce the cost of EO imagery.

An exponentially increasing volume of EO imagery is being captured every day from a variety of devices. The Committee on Earth Observation Satellites (CEOS) reports 286 Earth observation devices in orbit. The next Digital Globe satellite Worldview-3 scheduled for some time in 2014 will be capable of 31 cm resolution and can store two terabits of data between downloads. Two start-up satellite companies have already started putting satellite constellations in space, that promise to provide high frequency revisits to every point of the Earth's surface at low cost. Planet Labs and Skybox Imaging each plan to launch constellations of 24+ low cost satellites that will capture high-resolution imagery of every spot on earth many times per day. They promise to deliver the first ever HD-video of any spot on earth, allowing users to track changes—from traffic jams to deforestation—in near real time.

A few years ago I remember that the total amount of imagery being downloaded daily from all EO satellites was estimated to amount to about a terabyte. Now a single satellite could be responsible for a terabyte daily. If you add advances in aerial photogrammetry and the rapid development of UAV-based photogrammetry, we are looking at huge volumes of imagery every day, probably approaching a petabyte.

All of this data requires processing before it is made available to customers and end users. Currently this processing is time consuming and a significant proportion of it stil involves manual and semi-automated processing. This results in delays between the time the data is captured and when it is made available to customers which can range from days to weeks or even months. Customers are becoming impatient with the time it takes to process data and are increasingly pushing for near real-time availability.

At the PCI Geomatics Reseller Meeting in Ottawa Wolfgang Lück, whose company Forest Sense cc customizes image processing workflows and provides remote sensing training, gave an overview of the sensor web and how automating image processing is enabling near real-time imagery availability.

Wolfgang says that most of the sensor web including different types of satellites, ground-bases sensors, mobile devices and satellite monitoring systems called sentinels are in place.

EDRS Geostationary Relay and supercomputer

Wide area sytematic Optic/SAR monitoring

VHR SAR

Optical VHR/hyperspectral

Field sensors

Mobile devices

DRS (earth receiving station)

According to Wolfgang, the communications protocols and the standards (for example, the OGC Sensor Observation Service) are in place for these systems to talk to each other. Most of the satellite systems are in space, with just the sentinel systems still needing to go up. These are wide area systematic Optic/SAR monitoring systems being launched into space by ESA. On the ground there are receiving stations (DRS), field sensors, and mobile devices carried by people. The mobile devices capture as well as receive information.

Imagery requires involves complex processing to make it usable. With the huge volumes of data that the sensor web is capable of capturing this processing has to be automated if the data is to be available in near real-time at low cost. There are many actors that need to be accounted for, but the most important actors are end user customers (machines or human beings) because customers determine the products that are to be delivered.

Typical image processing workflow

There are different work flows depending on the sensors that are used and the final product requested by the customer. To provide a feel for just how complex this processing is and the challenges involved in automating these work flows, Wolfgang went through a typical work flow beginning with raw satellite imagery and following through to producing an orthorectified image suitable for classification, for example to identify different types of vegetation. Even for this pretty basic image processing work flow, there are many steps.

Ingestion of data from pick-up point

Binary data to supported format conversion

Relative radiometric correction and artifact removal

Band alignment

DN to ToA reflectance conversion

Haze cloud and water classification

Auto-ground control point collection

Orthorectification

DSM generation

Topographic correction

Spectral preclassification

Level 4 product generation

Image compositing / mosaicking

Delivery of data to pick-up point

In the past many of these steps were manual or semi-automated. The important breakthrough that Wolfgang was able to demonstrate is that he has been able to automate the entire process, which means that end user products, whether orthorectified images, digital surface models or classified vegetation maps are available much faster and at lower cost.

From Wolfgang's perspective the key to enabling this to happen is an image processing platform that provides a modular architecture, robust scripting, "big data" management, support for a broad range of sensor models (cameras), support for Linux as well as Windows, comprehensive image processing functionality, the ability to incorporate custom leading edge algorithms - integrated with an accessible user interface and API. In addition because this level of automation pushes the envelope, responsive support from the vendor is also essential.

In the example Wolfgang used a scripting language, Python or EASI, to implement a work flow primarily comprised of PCI-Geomatica modules, but also custom components implementing leading edge algorithms from current research.

The first step is format conversion. The data often comes in some proprietary format which the processing engine may not support. A converter is required to bring it into a format that the processing engine supports.

The next step is radiometric corrections and artifact removal. Forest Sense supports a wide range of systems put together by emerging space nations, such as African or Middle Eastern countries. In the example, you can see two lines lines are artifacts. New gains and biases have to be calculated for those lines and applied to the image. Other examples are random noise detection and dropped line removal.

Another problem is systematic noise. A fast Fourier transform (FFT) reveals low a certain pattern in the high frequency domain which is characteristic of systematic noise. We can detect that automatically, do a FFT, create a mask to filter it out, and then do a reverse FFT to reconstruct the corrected image.

The next correction is the flat field correction that ensures that the sensors are calibrated correctly relative to one another.

The next step is band alignment. The different bands need to be aligned because they are acquired at slightly different angles by different sensors.

The next step is total atmosphere reflectance correction. What the sensor actually gets is relative voltage, and that is converted to relative radiance, which need to be normalized to total atmosphere reflectance. This allows images acquired at different times, or from different sensors to be compared and to be used for quantitative analysis. But the effect of the atmosphere is still there. We can do atmospheric correction, but it is a bit of a black art and applied incorrectly can introduce artifacts. For this reason, many people in the domain prefer to use total atmospheric reflectance data because they can trust it.

At this point the image can be classified.

The first step in classifying the image is haze, water and cloud classification. To do this requires both blue and red bands. These bands correlate with one another very highly, but the blue band is affected stronger by haze and water vapour. What is called a clear sky vector can be applied to the image to calculate the haze vector and remove haze. The right of the side shows the haze that is removed from the image.

The next step is collection of ground control points (GCPs) so that accurate geolocations can be calculated. Unlike many systems which collect GCPs even over water or cloud which introduces errors, Wolfgang only collects ground control points over land.

The next step is preclassification. Spectral preclassification requires topographic normalization using a digital terrain model (DTM). The image has to be preclassfied into different surface types that have different BRDF (bidirectional reflectance distribution function) properties. Light reflects in different directions at different intensities on different surfaces at different wavelengths. This has to be done using bands that are not affected by topography, which makes this step quite complicated.

The slide shows a Landsat image of a hilly terrain with a lot of shade and exposed slopes which need to be normalized topographically. Traditionally people have used a range of vegetation indices, such as the normalized difference vegetation index (NDVI). From comparison with the DTM, it is possible to see that the NDVI is affected by topography. Although it is a ratio between bands, it is still possible to see shady and sunny slopes. The reason for this is that there is a lot of scattering in the blue band and shorter wavelengths but no scattering in the red and longer wavelengths so the ratio between the red band and the infrared band is not a pure ratio because the red band is affected by scattering.

The TVI index was developed to be resistant to topography. Of course it doesn't work in areas of full shade. A DSM is used to to calculate which areas are in complete shade so they can be masked out. At this point all of these techniques can be applied to generate an automatic spectral preclassification of the image which then can be used for topographic normalization. The skylight adaptive topographic normalization is based on techniques that Wolfgang developed using all of these preprocessing steps. In the corrected image it is still possible to still see some shady areas, but most of the shadow has been removed.

The next step is image compositing and mosaicing and the generation of Level 4 (geoinformation) products. These products might include vegetation indices, biophysical parameters, masks, pixel-based classifications or object-based classifications.

The final step is to package the data and deliver the data to the customer, typically by transmitting the imagery to a pickup point where it is catalogued.

At this point the product is available for the customer to collect from the pickup point.

Wolfgang demonstrated that downloading raw data and transforming it into something usable by a farmer, construction contractor, or insurance adjustor requires many steps and a lot of processing. This example of a typical image processing work flow shows just what nearly a petabyte of the raw imagery captured daily must go through to create something usable by the end user customer (people or machines). To get this to customers in near real-time is incredibly computationally intensive and involves rapidly processing massive volumes of data. This is why algorithmic performance is critical and why multiple processors with hyperthreading and most recently GPUs (graphics processing units) are being harnessed to improve throughput.